期刊
PHYSICAL REVIEW X
卷 7, 期 3, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevX.7.031060
关键词
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资金
- EPSRC [EP/N020669/1]
- Max Planck Society
- Irish Research Council (IRC)
- EPSRC Centre for Doctoral Training in Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES) [EP/L015854/1]
- Engineering and Physical Sciences Research Council [1612035, EP/N020669/1, EP/M022609/1, EP/L000253/1] Funding Source: researchfish
- EPSRC [EP/M022609/1, EP/N020669/1, EP/L000253/1] Funding Source: UKRI
We present a method that enables the identification and analysis of conformational Markovian transition states from atomistic or coarse-grained molecular dynamics (MD) trajectories. Our algorithm is presented by using both analytical models and examples from MD simulations of the benchmark system helix-forming peptide Ala 5, and of larger, biomedically important systems: the 15-lipoxygenase-2 enzyme (15-LOX-2), the epidermal growth factor receptor (EGFR) protein, and the Mga2 fungal transcription factor. The analysis of 15-LOX-2 uses data generated exclusively from biased umbrella sampling simulations carried out at the hybrid ab initio density functional theory (DFT) quantum mechanics/ molecular mechanics (QM/MM) level of theory. In all cases, our method automatically identifies the corresponding transition states and metastable conformations in a variationally optimal way, with the input of a set of relevant coordinates, by accurately reproducing the intrinsic slowest relaxation rate of each system. Our approach offers a general yet easy-to-implement analysis method that provides unique insight into the molecular mechanism and the rare but crucial (i.e., rate-limiting) transition states occurring along conformational transition paths in complex dynamical systems such as molecular trajectories.
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